A Takagi-Sugeno Fuzzy Neural Network-based Algorithm with Single-Channel EEG Signal for the Discrimination between Light and Deep Sleep Stage
Date Issued
2016
Date
2016
Author(s)
Tsai, Tsung-Han
Abstract
People pay attention to their sleep quality and sleep problems. When people don''t have enough or qualified sleep, these conditions may have negative impacts on people''s life and their efficiency in work. In order to solve these problems, many hospitals set up sleep quality centers where professional instruments and consultations are used to solve sleep problems, but these advanced instruments and human efforts cost a lot. Besides, people must be stuck with many electrodes to collect signals, such as electroencephalogram (EEG), electrocardiogram (ECG), electromyography(EMG), oxygen desaturation index and so on, before people receive their diagnosis and analysis for their sleep conditions. During the examination, people with electrodes feel uncomfortable due to lots of electrodes influencing their sleep, so the results might be incorrect and unable to reflect the real conditions. To address these problems, we propose the Takagi-Sugeno fuzzy neural network-based algorithm with single-channel EEG signal for the discrimination between light and deep sleep stage. This main algorithm is using the single-channel EEG to combine signal processing and Takagi-Sugeno neural network to discriminate between light and deep sleep. The advantage of using the single-channel EEG is decreasing people''s uncomfortable feeling, reflecting the real sleep conditions, and increasing the accuracy by using two electrodes to get the EEG signal.
Subjects
Sleep quality
Electroencephalogram
Light sleep
Deep sleep
Energy
Takagi-Sugeno neural network
Type
thesis